摘要
Abstract
Objective The aim of this study was to investigate whether a multimodal artificial intelli-gence diagnostic model can be constructed based on ultrasound images and urinary cytology to improve diagnostic sensitivity and assist in reducing the use of cystoscopy.Methods This was a single-center retrospective study that included 2056 patients who underwent both bladder ultrasound examination and urinary cytology examination from January 2018 to September 2023 for model training and validation.The gold standard was determined based on the patients'histopathological results,and patients with negative results needed to be followed up for 6 months to confirm non-cancer.Firstly,we constructed an AI diagnostic model for bladder cancer based on ResNet model and ultrasound images.We used pre-trained weights on ImageNet as the initialization of model weights.Random gradient descent and cross-entropy loss were used for network weight adjustment and algorithm optimization.After the ultrasound AI model output the diagnostic score,we combined it with the results of urinary cytology diagnosis and clinical risk factors based on Logistic regression to construct a multimodal diagnostic model,and output the final diagnostic probability for each individual.The effectiveness of the model was then validated in the valida-tion set and subgroups(including different stages,grades,and clinical scenarios).The final multimodal model was named BCaUSNet.Results The BCaUSNet model had a diagnostic sensitivity of 0.896(95%CI:0.839-0.938)and an area under the curve of 0.917(95%CI:0.891-0.942)in the valida-tion set.In the scenario of recurrence monitoring,the model's sensitivity could reach 0.821(95%CI:0.631-0.939),and the negative predictive value could reach 0.896(95%CI:0.773-0.965),which could assist in reducing the use of cystoscopy with a high degree of certainty.In tumors with low malignant potential and low-grade tumors where urinary cytology is difficult to diagnose,the BCaUSNet model increased their sensitivity to 71.4%and 93.3%,respectively.In non-muscular invasive tumors and small tumors(<1.5 cm)that are easily missed by ultrasound,the BCaUSNet model increased their sensitivity to 89.5%and 87.5%,respectively.Conclusion The construction of a multimodal artificial intelligence diagnostic model for bladder cancer based on ultrasound images and urinary cytology has high diagnostic sensitivity,which helps to reduce missed diagnosis of bladder cancer,reduce the use of cystoscopy,and has good clinical utility and innovative significance.关键词
膀胱癌/超声影像/尿液细胞学/人工智能/多模态诊断模型Key words
bladder cancer/ultrasound imaging/urinary cytology/artificial intelligence/multi-modal diagnostic model分类
临床医学
